ESCRS - POCAT203 - A Novel Protocol For Anterior Capsule Staining In Cataract Surgery Using Selective Laser Capsulotomy

A Novel Protocol For Anterior Capsule Staining In Cataract Surgery Using Selective Laser Capsulotomy

Published 2026 - 44th Congress of the ESCRS

Reference: POCAT203 | Type: Free paper | DOI: 10.82333/cehf-5n94

Authors: A/Prof. Ahmed Samir* 1

1magrabi eye hospital,jeddah,Saudi Arabia

Purpose

Purpose: To introduce and evaluate a new protocol for anterior capsule staining during cataract surgery utilizing selective
laser capsulotomy, aiming to enhance visualization, improve surgical precision, and optimize outcomes, particularly in
complex cases.

Setting

Setting: magrabi eye hospital jeddah ksa

Methods

Methods: A prospective study was conducted on patients undergoing cataract surgery with laser-assisted capsulotomy
or CapsuLaser. The new protocol involved a modified technique for anterior capsule staining using trypan blue dye
following laser capsulotomy. Key parameters assessed included capsule visualization, ease of staining, intraoperative
complications, and postoperative visual outcomes. Comparative analysis was performed between conventional manual
staining techniques and the proposed protocol.

Results

Results: The novel staining approach provided uniform and enhanced capsule visualization, facilitating precise
capsulorhexis completion. The staining process was more controlled, reducing dye dispersion into the anterior chamber.
Cases with mature or white cataracts particularly benefited, with improved anterior capsule identification. There were no
significant intraoperative complications, and postoperative visual outcomes were comparable or superior to conventional
techniques.

Conclusions

Conclusions: The proposed anterior capsule staining protocol in conjunction with selective laser capsulotomy offers
improved surgical control, enhanced visualization, and potential safety advantages. This technique may be particularly
beneficial in challenging cataract cases, warranting further large-scale validation.